Various approaches have been proposed for increasing the network capacity of communication networks. One example approach includes identifying one or more network-wide optimization solutions. Theoretically, significant gains could be obtained by performing these optimizations for an entire network. However, in large cellular or other networks, information sharing limitations and centralized processing limitations often prevent network-wide optimizations from being identified and implemented by a single centralized entity.
For these and other reasons, a network can instead be divided into smaller non-overlapping sections called clusters, and an optimization solution can be calculated for each cluster. Unfortunately, clusters are not always isolated from one another, and a node in one cluster can interfere with neighboring nodes in other clusters. This interference can cause local optimization solutions within the clusters to move away from a global optimization solution. As a result, the local optimization solutions typically do not approximate the global optimization solution. Because of this, the overall network has less network capacity using the local optimization solutions than would be obtainable using the global optimization solution.